Congestion control algorithms (CCAs) play a critical role in determining transmission quality. With their rapid evolution during the past few decades, understanding the CCA landscape on the Internet has become increasingly essential for network advancement. Traditional CCA census tools, however, rely heavily on manual configuration and construction, necessitating significant human effort to keep pace with the introduction of new CCAs.

Style is prone to experience but hard to formulate, even for design professionals. The current style classification (SC) methods can be categorized into mono-tasking (style) and multi-tasking (style, artists, genre, and other additional clues). However, the style strength spread-out phenomenon challenges them by the uncertain noises’ distribution and unstable distinguishable feature representation. The multiple sub-styles make the principal style recognition hard for current SC approaches with lower accuracy. This article proposes the community transferrable representation learning (CTRL) framework inspired by community detection and style transfer studies. The community feature is the transferrable feature embeddings in latent space through all samples within the same style concept. Therefore, instead of learning the unique style features, we introduce the learnable transferrable embeddings, whose distance will be smaller for the intraclass and larger for the interclass, to represent the principal style concept. There are two generative processes, p-AE and q-AE, as the cooperative operations to learn the community styles by the proposed diamond cells (D-cells) in a stacked way. The proposed community loss includes two terms: the invariance term is used to make the principal style embedding consistent, and the redundancy reduction term can decorate the different vector embeddings of sub-styles. The style classification and style feature learning model are optimized simultaneously in a semi-supervised way to learn the class-level style representation. We conduct experiments on six style datasets (three oil paintings, one architecture, one fashion, and one general image). Results show that the proposed framework brings a performance gain of 2%–7% in terms of accuracy compared with the SOTA approaches.